Documents about Error Bars

  • 3 Pages

    lec20

    Dallas, PSY 3393

    Excerpt: ... Dates PSY 3393 Experimental Projects Spring 2008 Dr. Peter Assmann Draft Introduction section due date: Thu Mar 27 (email is fine) Draft Methods section due date: Tue Apr 8 Hypothetical Data Writeup Before you collect the data for your second project, use the predictions you made in the Introduction section to develop (i.e., make up) a set of hypothetical data that fit these predictions. predictions Note: the data you report in your actual project must be collected, not made up! Write up these hypothetical results in APA format. Use "dummy" (hypothetical) values for F, df, and p. Date due: April 15 Introduction section The Introduction section places the study in a broader context and shows the reader where it fits into the literature. What do we already know about the topic? What missing pieces does the study fill in? Why is it important? Error Bars Error bars reflect the variability in scores around the means in two or more conditions. D Download the Excel l d th E l sprea ...

  • 4 Pages

    discussion-sol

    Maryland, ASTR 220

    Excerpt: ... as some of the major mass extinctions. The question is whether or not this is coincidence. There is some evidence against this idea: we have many geologic processes on-going today, such as volcanism and sea floor spreading, and yet we have not had a large impact (to our knowledge) since the hypothetical Eocene-Oligocene mass extinction about 35 million years ago (Ch. 11, Table 4). Is the current geologic activity on the Earth still powered by that impact? Or is there another cause? Part 2, dealing with the article on the research by Gerta Keller. 1. From NCC, list at least four different age determinations of the Chicxulub crater, including their error bars . ( Error bars are the plus/minus numbers after the ages. They indicate the range of ages that the result includes. For example, if I measured your age as 21 1yr, I would consider my measurement to be accurate if your age was 20, 21, or 22 yrs.) Give the page numbers where you got the ages. Do you think that it would be easy for Dr. Keller to distinguish l ...

  • 6 Pages

    exp4

    Michigan State University, PHY 191

    Excerpt: ... ently close? 4) Next, extend your spreadsheet to calculate the deviations (= residuals), and their squares, from the linear fit, and use them to evaluate Eqs 8.15 - 8.17. Eq 8.15 is what you use to estimate y (the uncertainty in y measurements). This equation assumes y to be the same for each individual measurement. Its the method to use when you dont have any other way to estimate y. Eq 8.16-8.17 relate an estimate of the y (whether from 8.15, or from another method) to find the uncertainty in the intercept and slope. Kgraph uses methods like Eqs 8.15-17 to estimate parameter errors unless the weighted fit box is checked, in which case the data error bars are used. 5) Now calculate t to test to see whether your fit slope is consistent with your handmeasured slope. 1. Goals 1.1 To quantitatively study the time dependence of the velocity and position of a body falling freely under the influence of gravity. 1.2 To use least squares fitting methods to obtain best values and uncertainties for ...

  • 6 Pages

    exp4

    Michigan State University, PHY 191

    Excerpt: ... ntly close? 4) Next, extend your spreadsheet to calculate the deviations (= residuals), and their squares, from the linear fit, and use them to evaluate Eqs 8.15 - 8.17. Eq 8.15 is what you use to estimate y (the uncertainty in y measurements). This equation assumes y to be the same for each individual measurement. Its the method to use when you dont have any other way to estimate y. Eq 8.16-8.17 relate an estimate of the y (whether from 8.15, or from another method) to find the uncertainty in the intercept and slope. Kgraph uses methods like Eqs 8.15-17 to estimate parameter errors unless the weighted fit box is checked, in which case the data error bars are used. 5) Now calculate t to test to see whether your fit slope is consistent with your handmeasured slope. 1. Goals 1.1 To quantitatively study the time dependence of the velocity and position of a body falling freely under the influence of gravity. 1.2 To use least squares fitting methods to obtain best values and uncertainties for ...

  • 23 Pages

    Lab_4_2009

    Swarthmore, PHYSICS 50

    Excerpt: ... formulas. Next we want to obtain an associated error bar, a j , for the curve fit parameter aj. Using the standard law of propagation of errors, we have 2 N a j 2 2 aj = i i =1 yi using the results above for a1 and a2 and doing some algebra we get a1 = Notice that a j is independent of yi . the parameters is S x 2 x 2 2 ( x ) , a2 = S S x 2 ( x ) 2 If the error bars on the data are constant ( i = 0 ), the error in a1 = 0 N x = x2 x 2 x 2 , a2 = 0 N 1 x 2 x 2 1 1 x , x2 = N x2 N Finally, if the data set does not have an associated set of error bars , we can estimate 0 from the data Note that this sample variance is normalized to N-2 since we have already extracted tow parameters a1 and a2 from the data. Many non-linear curve-fitting problems may be transformed into linear problems by a simple change of variable. For example, Page 4 1 N 2 (standard-deviat ...

  • 1 Pages

    10p

    UPenn, VHM 801

    Excerpt: ... Notes for Exercises in Session 10 12:5,27x,56,57,21; 6:86; 12:22,34,35; 15:24 (12:29x; 15:33,37; midterm 2001); note suggested order, review of analysis after the ANOVA table, individual work on the exercises, time for questions on home assignment III. Minitab for 1-way ANOVA:1 Stat-ANOVA-One Way (best menu to use; not Unstacked!); for Bonferroni corrections: use Comparisons menu and Fisher (LSD) method with manually corrected error level , to plot group means with error bars : Stat-ANOVA-Interval Plot (with groups), right-click on interval bar, Edit Interval Bar-Options-Pool to display condence intervals based on pooled standard dev. Notes and questions for specic exercises: 12.27x: from lecture 10L; solution found under exercise 12.32 in lecture 10 of 2004, 15.24: analyze also by 1-way ANOVA procedure and pay attention to model assumptions, 12.29x: no data available (only summaries), so analysis must be done by hand (calculator); forget about R2, midterm 200 ...

  • 2 Pages

    v18n2p91-92

    Indiana State, V 18

    Excerpt: ... ). Note that the Opticells apparently mimic a static flask condition rather than a shaking flask condition. These results suggest that our culture conditions support the growth of C. elegans and S. cerevisiae in a manner comparable to conventional culturing methods. Figure 2. Growth of C. elegans in OptiCells with various starting densities. Animals were inoculated to a density of 10, 100, or 1000 worms/ml 10 ml of CeMM in OptiCells and incubated at 20 C. Worms were periodically withdrawn and counted. Error bars show the standard deviation from the mean. N = 3. Figure 3. Growth of S. cerevisiae in Opticells. Stationary phase S. cerevisiae BY4743 were diluted 1:1000 in YPD broth, transferred to shaker flasks or Opticells, and incubated at 30 C. The flasks were incubated either with shaking or statically. The OptiCells were incubated statically with a 2mm space between replicate Opticells. Growth was monitored over time by measuring the optical density at 600 nm. Error bars depict the standard ...

  • 1 Pages

    Homework2

    NYU, MONTECARLO 07

    Excerpt: ... Monte Carlo Methods, Fall 2007, Courant Institute, NYU Homework 2, Due October 16 1. Verify the recurrence relation for vk (x) in Section 3.2 of the SDE lecture notes. Use this recurrence relation to verify by induction that Lvk = -kvk . 2. Read the Monte Carlo lecture notes chapter from Scientific Computing to see how to make error bars for simple samplers. 3. Let f (x) = C 1 - x2 be the marginal distribution for the pair (X, Y ) uniformly distributed within the unit disk X 2 + Y 2 1. Show that sampling X by rejection from the one dimensional uniform distribution gives the same algorithm as sampling the pair (X, Y ) within the unit disk by rejection as in homework 1. 4. Make a plot of a Monte Carlo estimate of the exponential moment function Z() = Ef eX for in the range 0 10. Use one set of samples of f for the whole curve. Plot the estimate and the error bar for a not very large L (sample size) value and a fairly large value (two plots, one for each L value, each having the estimate together with a o ...